The Econometrics of Impact Evaluation course covers the most commonly used statistical techniques used to undertake causal analyses in quantitative empirical economics. The objective is to understand each technique, its limitations, and (un)suitability in different contexts. The course starts with randomized controlled trials, considered by some as the gold standard for causal inference, and moves on to techniques such as regressions, propensity score matching, difference-in-differences, instrument variables, and regression discontinuity that rely on natural experiments or quasi-experimental variation in the real world to tease out causal effects. The course includes applications of each technique in real world settings, with many examples embedded within the Indian context. It would enable students to use survey data in order to understand the implications of specific events or government policy changes and enable them to provide evidence-based inputs to practitioners on whether or not to scale up field interventions.